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Adjusted Empirical Likelihood Estimation of Distribution Function and Quantile with Nonignorable Missing Data

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Journal of Systems Science & Complexity 2018年第3期31卷 820-840页

作者： DING Xianwen tang niansheng Key Laboratory of Statistical Modeling & Data Analysis of Yunnan Province Yunnan University Kunming 650500 China Department of Mathematics Jiangsu University of Technology Changzhou 213001 China

This paper considers the estimation problem of distribution functions and quantiles with nonignorable missing response data. Three approaches are developed to estimate distribution functions and quantiles, i.e., the Horvtiz-Thompson-type method, regression imputation method and augmented inverse probability weighted approach. The propensity score is specified by a semiparametric expo- nential tilting model. To estimate the tilting parameter in the propensity score, the authors propose an adjusted empirical likelihood method to deal with the over-identified system. Under some regular conditions, the authors investigate the asymptotic properties of the proposed three estimators for distri- bution functions and quantiles, and find that these estimators have the same asymptotic variance. The jackknife method is employed to consistently estimate the asymptotic variances. Simulation studies are conducted to investigate the finite sample performance of the proposed methodologies.

关键词： Adjusted empirical likelihood distribution estimation exponential tilting model nonig-norable missing data quantile.

Consistency and asymptotic normality of profilekernel and backfitting estimators in semiparametric reproductive dispersion nonlinear models

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Science China Mathematics 2009年第4期52卷 757-770页

作者： tang niansheng CHEN XueDong WANG XueRen Department of Statistics Yunnan UniversityKunming 650091China Faculty of Science Huzhou Normal CollegeHuzhou 313000China

Semiparametric reproductive dispersion nonlinear model (SRDNM) is an extension of nonlinear reproductive dispersion models and semiparametric nonlinear regression models, and includes semiparametric nonlinear model and semiparametric generalized linear model as its special cases. Based on the local kernel estimate of nonparametric component, profile-kernel and backfitting estimators of parameters of interest are proposed in SRDNM, and theoretical comparison of both estimators is also investigated in this paper. Under some regularity conditions, strong consistency and asymptotic normality of two estimators are proved. It is shown that the backfitting method produces a larger asymptotic variance than that for the profile-kernel method. A simulation study and a real example are used to illustrate the proposed methodologies.

关键词： asymptotic normality backfitting method consistency profile-kernel method semiparametric reproductive dispersion nonlinear models 62G05 62G08 62G20

A New Approach for Regression Analysis of Multivariate Current Status Data with Informative Censoring

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Communications in Mathematics and Statistics 2023年第4期11卷 775-794页

作者： Huiqiong Li Chenchen Ma Jianguo Sun niansheng tang Department of Statistics Yunnan UniversityKunming 650091People’s Republic of China Department of Statistics University of MissouriColumbiaMO 65211USA

Regression analysis of interval-censored failure time data has recently attracted a great deal of attention partly due to their increasing occurrences in many *** this paper,we discuss a type of such data,multivariate current status data,where in addition to the complex interval data structure,one also faces dependent or informative censor*** inference,a sieve maximum likelihood estimation procedure is developed and the proposed estimators of regression parameters are shown to be asymptotically consistent and *** the implementation of the method,an EM algorithm is provided,and the results from an extensive simulation study demonstrate the validity and good performance of the proposed inference *** an illustration,the proposed approach is applied to a tumorigenicity experiment.

关键词： Additive hazards model Current status data Informative censoring

Statistical inference for nonignorable missing-data problems:a selective review

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Statistical Theory and Related Fields 2018年第2期2卷 105-133页

作者： niansheng tang Yuanyuan Ju Key Lab of Statistical Modelling and Data Analysis of Yunnan Province Yunnan UniversityKunming650091People’s Republic of China

Nonignorable missing data are frequently encountered in various settings, such as economics,sociology and biomedicine. We review statistical inference for nonignorable missing-data problems, including estimation, influence analysis and model selection. For estimation of meanfunctionals, we review semiparametric method and empirical likelihood (EL) approach. For estimation of parameters in exponential family nonlinear structural equation models, we introduceexpectation-maximisation algorithm, Bayesian approach, and Bayesian EL method. For influenceanalysis, we investigate the case-deletion method and local influence analysis method fromthe frequentist and Bayesian viewpoints. For model selection, we present the modified Akaikeinformation criterion and penalised method.

关键词： Bayesian method empirical likelihood method expectation-maximisation algorithm local influence analysis missing data model selection

Rejoinder: statistical inference for non-ignorable missing-data problems: a selective review

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Statistical Theory and Related Fields 2018年第2期2卷 146-149页

作者： niansheng tang Yuanyuan Ju Key Lab of Statistical Modeling and Data Analysis of Yunnan Province Yunnan UniversityKunming 650091People’s Republic of China

We thank five discussants for their thoughtful *** have made significant contributions to the general theme raised in our *** will try our best to answer each of points that five discussers have made.

关键词： thank raised missing

多元约束Welsch-Kuh距离与多元约束Cook距离

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工程数学学报 2000年第1期17卷 1-6页

作者：唐年胜韦博成云南大学成人教育学院昆明650091 东南大学应用数学系南京210096

对多元约束线性回归模型 ,本文讨论了约束最小二乘估计的影响问题 ,定义了度量影响的多元约束Cook距离和多元约束 W- K统计量 ,给出了其分布 ,建立了约束 W- K统计量与约束广义相关系数之间的联系。

关键词：多元W-K统计量多元Cook距离多元线性回归

SOME ASYMPTOTIC INFERENCE IN QUASI-LIKELIHOOD NONLINEAR MODELS:A GEOMETRIC APPROACH

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Applied Mathematics(A Journal of Chinese Universities) 2000年第2期15卷 173-183页

作者： WeiBocheng tangniansheng WangXueren Dept.ofMath. SoutheastUniv.Nanjing210096. Dept.ofStatistics YunanUniv.Kunming650091.

A modified Bates and Watts geometric framework is proposed for quasi\|likelihood nonlinear models in Euclidean inner product *** on the modified geometric framework,some asymptotic inference in terms of curvatures for quasi\|likelihood nonlinear models is *** previous results for nonlinear regression models and exponential family nonlinear models *** extended to quasi\|likelihood nonlinear models.

关键词： Curvature array quasi\|information quasi\|likelihood nonlinear models stochastic expansion variance.

带有不可忽略缺失数据的半参数再生散度模型的贝叶斯分析

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系统科学与数学 2010年第10期30卷 1334-1350页

作者：陈雪东唐年胜浙江湖州师范学院湖州313000 云南大学统计系昆明650091

半参数再生散度模型是再生散度模型和半参数回归模型的推广,包括了半参数广义线性模型和广义部分线性模型等特殊类型.讨论的是该模型在响应变量和协变量均存在非随机缺失数据情形下参数的Bayes估计和基于Bayes因子的模型选择问题.在分析... 详细信息

半参数再生散度模型是再生散度模型和半参数回归模型的推广,包括了半参数广义线性模型和广义部分线性模型等特殊类型.讨论的是该模型在响应变量和协变量均存在非随机缺失数据情形下参数的Bayes估计和基于Bayes因子的模型选择问题.在分析中,采用了惩罚样条来估计模型中的非参数成分,并建立了Bayes层次模型;为了解决Gibbs抽样过程中因参数高度相关带来的混合性差以及因维数增加导致出现不稳定性的问题,引入了潜变量做为添加数据并应用了压缩Gibbs抽样方法,改进了收敛性;同时,为了避免计算多重积分,利用了M-H算法估计边缘密度函数后计算Bayes因子,为模型的选择比较提供了一种准则.最后,通过模拟和实例验证了所给方法的有效性.

关键词：不可忽略缺失数据半参数再生散度模型惩罚样条压缩Gibbs抽样 M-H算法

带不可忽略缺失数据的再生散度随机效应模型的Bayes估计

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宁夏大学学报（自然科学版） 2011年第3期32卷 193-197页

作者：和燕彭燕梅唐年胜楚雄师范学院计算机科学与技术系云南楚雄675000 楚雄师范学院地理科学与旅游管理系云南楚雄675000 云南大学数学与统计学院云南昆明650091

在响应变量带有不可忽略缺失数据的前提下得到非线性再生散度随机效应模型的Bayes方法.缺失数据机制由Logistic回归模型定义,根据Gibbs抽样技术和MH算法得到模型参数、随机效应因子以及缺失数据机制中回归系数的联合Bayes估计,并进行了... 详细信息

在响应变量带有不可忽略缺失数据的前提下得到非线性再生散度随机效应模型的Bayes方法.缺失数据机制由Logistic回归模型定义,根据Gibbs抽样技术和MH算法得到模型参数、随机效应因子以及缺失数据机制中回归系数的联合Bayes估计,并进行了实例分析.

关键词：不可忽略缺失数据机制非线性再生散度随机效应模型 Bayes方法 MCMC